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# to run in google colab
import sys
if 'google.colab' in sys.modules:
! apt-get install subversion
! svn export https://github.com/YoniChechik/AI_is_Math/trunk/c_02_image_processing/Tour_Eiffel.jpg
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import numpy as np
import cv2
from matplotlib import pyplot as plt
fig_size = (10,10)
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# to run interactively with vscode
import os
if os.getcwd().endswith("AI_is_Math"):
os.chdir("c_02_image_processing")
Get basic image:
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def plot_im(img, title):
plt.figure(figsize=fig_size)
plt.imshow(img)
plt.title(title)
plt.xticks([])
plt.yticks([])
img = cv2.imread("Tour_Eiffel.jpg")
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plot_im(img, "orig")
mean filter¶
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def mean_kernel_smoothing(img, sz):
kernel = np.ones((sz, sz))/(sz**2)
dst = cv2.filter2D(img, -1, kernel)
plot_im(dst, str(sz)+'X'+str(sz)+" mean kernel")
mean_kernel_smoothing(img, 5)
mean_kernel_smoothing(img, 10)
mean_kernel_smoothing(img, 20)
gaussian filter¶
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def gauss_blur(img, k_sz, sigma=-1, is_plot_kernel=False):
blur = cv2.GaussianBlur(img, (k_sz, k_sz), sigma)
plot_im(blur, "gaussian kernel with kernel_size="
+ str(k_sz)+r", $\sigma$=" + str(sigma))
if is_plot_kernel:
gauss_ker = cv2.getGaussianKernel(k_sz, sigma)
plt.figure(figsize=(fig_size[0]/2, fig_size[1]/2))
plt.plot(gauss_ker)
plt.title("corresponding gaussian kernel")
gauss_blur(img, 5, is_plot_kernel=True)
gauss_blur(img, 21, is_plot_kernel=True)
gauss_blur(img, 21, 1, is_plot_kernel=True)
madian filter¶
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def median_blur(img, k_sz):
res = cv2.medianBlur(img, k_sz)
plt.figure(figsize=fig_size)
plot_im(res, "median filter with kernel_size="+str(k_sz))
median_blur(img, 5)
noise addition func¶
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def noisy(noise_typ, image, gauss_var=1000, s_p_ratio=0.04):
# modified from: https://stackoverflow.com/questions/22937589/how-to-add-noise-gaussian-salt-and-pepper-etc-to-image-in-python-with-opencv
if noise_typ == "gauss":
mean = 0
sigma = gauss_var**0.5
gauss = np.random.normal(mean, sigma, image.shape)
res = image + gauss
noisy = np.uint8(res)
return noisy
elif noise_typ == "s&p":
# this implementation is not entirely correct because it assumes that
# only 0 OR 255 values are S&P noise.
out = np.copy(image)
# Salt mode
mask = np.random.randint(101, size=image.shape) <= s_p_ratio*100/2
out[mask] = 255
# Pepper mode
mask = np.random.randint(101, size=image.shape) <= s_p_ratio*100/2
out[mask] = 0
return out
gaussian noise tests¶
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gauss_noise_im = noisy("gauss", img, gauss_var=70)
plt.figure(figsize=fig_size)
plt.imshow(gauss_noise_im)
plt.title('original image + gaussian noise')
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gauss_blur(gauss_noise_im, 3)
gauss_blur(gauss_noise_im, 5)
gauss_blur(gauss_noise_im, 11)
median_blur(gauss_noise_im, 5)
salt and pepper noise test¶
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s_p_noise_im = noisy("s&p", img, s_p_ratio=0.04)
plt.figure(figsize=fig_size)
plt.imshow(s_p_noise_im)
plt.title('original image + s&p noise')
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gauss_blur(s_p_noise_im, 5)
median_blur(s_p_noise_im, 3)
median_blur(s_p_noise_im, 5)
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